مقایسهٔ روشها
روشهای انتخابی خود را کنار هم مرور کنید؛ ردیفهای متفاوت برجسته شدهاند.
| مدلسازی علی پویا× | تحلیل شبکه مغزی با استفاده از نظریه گراف× | |
|---|---|---|
| حوزه | تصویربرداری عصبی | تصویربرداری عصبی |
| خانواده | Process / pipeline | Process / pipeline |
| سال پیدایش≠ | 2003 | 2009 |
| پدیدآور≠ | Karl J. Friston | Ed Bullmore |
| نوع≠ | Causal modeling pipeline for neuroimaging | Brain network graph analysis pipeline |
| منبع بنیادین≠ | Friston, K. J., Harrison, L., & Penny, W. (2003). Dynamic causal modelling. NeuroImage, 19(4), 1273–1302. DOI ↗ | Bullmore, E., & Sporns, O. (2009). Complex brain networks: graph theoretical analysis of structural and functional systems. Nature Reviews Neuroscience, 10(3), 186–198. DOI ↗ |
| نامهای دیگر≠ | DCM, Dynamic Causal Model | graph theory, brain network analysis, network neuroscience |
| مرتبط≠ | 2 | 3 |
| خلاصه≠ | Dynamic Causal Modeling (DCM) is a Bayesian framework for specifying and inverting generative models of brain connectivity from neuroimaging data. Introduced by Karl Friston and colleagues in 2003, DCM treats brain regions as dynamical systems and estimates effective connectivity by fitting observed fMRI time series to a biophysically plausible model of neuronal interactions. | Graph Theoretical Brain Network Analysis applies network science to understand brain organization, treating the brain as a complex network of interconnected nodes (regions) and edges (connections). Formalized by Bullmore and Sporns in 2009, graph analysis reveals fundamental organizational principles—modularity, efficiency, resilience—that characterize healthy and diseased brains. |
| ScholarGateمجموعهداده ↗ |
|
|